Classical machine learning methods have demonstrated strong performance in email phishing detection, but their ability to generalize against sophisticated phishing behaviors remains limited. Recent advances in quantum machine learning suggest that quantum kernel methods may capture complex feature interactions more effectively. This paper presents a batchwise ensemble evaluation framework that independently trains classical Support Vector Machine (SVM) and Quantum Support Vector Machine (QSVM) learning models using identical datasets. Due to quantum computational constraints, training is performed on fixed-size data batches and final predictions are obtained through majority voting across batches. Experimental results show that the QSVM ensemble reduces false negatives by 79 % compared to classical SVM, although at significantly higher computational cost, highlighting both the potential and current limitations of quantum kernel methods in the NISQ era.